ReviewComputational and structural biotechnology journal2025
The role of artificial intelligence and machine learning in predicting and combating antimicrobial resistance.
Review in Computational and structural biotechnology journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed.
- Development and Validation of a Computer Vision-Based Artificial Intelligence System (FenoParasite) for the Microscopic Detection ofTropical medicine and infectious disease · 2026Article
- Artificial Intelligence and Bioengineering Approaches for Antimicrobial Resistance Prediction.Medicina (Kaunas, Lithuania) · 2026Review
- Comparison of OneChoice AI-Based Clinical Decision Support Recommendations with Infectious Disease Specialists and Non-Specialists for Empirical Urinary Tract Infection Therapy in Lima, Peru.Diagnostics (Basel, Switzerland) · 2026Article
- Beyond the Barrier: Overcoming Ocular Antimicrobial Resistance Through AI and Novel Therapeutics.International journal of molecular sciences · 2026Review
- Reimagining antimicrobial resistance: AI-driven predictive epidemiology and the C-AMRE framework for next-generation antibiotic discovery.The Journal of antibiotics · 2026Review
- Recent advancements in artificial intelligence applications for the mitigation of antimicrobial resistance: challenges and opportunities.JAC-antimicrobial resistance · 2026Review
- Addressing antimicrobial resistance: Current challenges, emerging strategies, and an AI-powered, community-driven approach.PNAS nexus · 2026Review
- Microbial biobanking: safeguarding the tiny treasures for sustainable human welfare.Folia microbiologica · 2026Review
- Phenotype-Guided Nanotherapeutic Strategies for Carbapenem-ResistantPharmaceutics · 2026Review
- Harnessing artificial intelligence for antimicrobial discovery and optimization.Current opinion in microbiology · 2026Review
- Two Worlds, One Battle: How Bacteria and Malignancies Converge on Drug Resistance.International journal of molecular sciences · 2026Review
- Improving the Precision of Etiological Diagnosis in Bacterial Infections Using Molecular Technologies: A Comparative Analysis of Platforms, AI Integration, and Point-of-Care Deployment.International journal of molecular sciences · 2026Review
- Next-Generation Target Discovery in ESKAPE Pathogens: An AI-Driven Framework from Omics-Based to Systems-Level Modeling and Clinical Translation.Antibiotics (Basel, Switzerland) · 2026Review
- Artificial intelligence for antimicrobial resistance: advancing reproducibility, interpretability, and clinical deployment.Briefings in bioinformatics · 2026Review
- Quorum Sensing and Quorum Quenching in Pseudomonas aeruginosa and Staphylococcus aureus Infections: Therapeutic Potential, Limitations and Clinical Challenges.Antibiotics (Basel, Switzerland) · 2026Review
- Antimicrobial Resistance Along the Food Chain: Spread and Integrated Strategies for Mitigation and Control.Antibiotics (Basel, Switzerland) · 2026Review
- AI-Powered Microscopic Diagnostic Techniques forJournal of dentistry (Shiraz, Iran) · 2026Article
- The infectome framework: linking polymicrobial ecology and biofilm dynamics to precision diagnostic approaches.Infection · 2026Review
- Applying biotechnology to overcome cancer drug resistance and improve public health outcomes.Osong public health and research perspectives · 2026Article
- Vancomycin resistance in gram-positive infections: evolutionary strategies of survival.Archives of microbiology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Antimicrobial resistance (AMR) is a major threat to global public health. The current review synthesizes to address the possible role of Artificial Intelligence and Machine Learning (AI/ML) in mitigating AMR. Supervised learning, unsupervised learning, deep learning, reinforcement learning, and natural language processing are some of the main tools used in this domain. AI/ML models can use various data sources, such as clinical information, genomic sequences, microbiome insights, and epidemiological data for predicting AMR outbreaks. Although AI/ML are relatively new fields, numerous case studies offer substantial evidence of their successful application in predicting AMR outbreaks with greater accuracy. These models can provide insights into the discovery of novel antimicrobials, the repurposing of existing drugs, and combination therapy through the analysis of their molecular structures. In addition, AI-based clinical decision support systems in real-time guide healthcare professionals to improve prescribing of antibiotics. The review also outlines how can AI improve AMR surveillance, analyze resistance trends, and enable early outbreak identification. Challenges, such as ethical considerations, data privacy, and model biases exist, however, the continuous development of novel methodologies enables AI/ML to play a significant role in combating AMR.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.